I was just wondering why regression problems are called "regression" problems. What is the story behind the name? One definition for regression: "Relapse to a less perfect or developed state."

Dec 4, 2014 · When we say, to regress Y Y against X X, do we mean that X X is the independent variable and Y the dependent variable? i.e. Y = aX + b Y = a X + b.

I'm trying to fit a multiple linear regression model to my data with couple of input parameters, say 3.

What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?

Mar 3, 2020 · One of the predictors in my logistic model has been log transformed. How do you interpret the estimated coefficient of the log transformed predictor and how do you calculate …

Another beauty of this way of looking at things is how it can help us understand ridge regression. When we want to really understand regression, it almost always helps to think of it …

Oct 3, 2021 · Although one can compute a single regression for all data points, if you include model assumptions such as i.i.d. normal errors, the for all points combined can't be "correct" if …

Oct 19, 2011 · LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value …

Mar 17, 2016 · Otherwise, just try logistic regression first and see how you do with that simpler model. If logistic regression fails you, try an SVM with a non-linear kernel like a RBF. EDIT: …

Aug 24, 2021 · This is because any regression coefficients involving the original variable - whether it is the dependent or the independent variable - will have a percentage point change …